Improvement of Unconstrained Appearance-Based Gaze Tracking with LSTM
Guoxu Li, Lihong Dai, Qing Hong Gao, Hongwei Gao, Zhaojie Ju · 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) · 2022
Gaze tracking is not only an important research direction in computer vision but also an important non-verbal clue in human life. What is important is that the direction of gaze can be used as a reference for judging a person’s intentions. In order to improve the accuracy of predicting gaze direction, a model of 3D gaze tracking based on bidirectional Long Short-Term Memory (LSTM) is proposed in this paper. The backbone network of the model is ResNet and its variants. The output of the model is the angular error of gaze direction. To improve the accuracy of the model prediction, the attention mechanism is adopted in this work. The ablation experiments are conducted on the selected Gaze360, which is a dataset with sufficiently large and diverse data. The angular error of the proposed model decreases from 13.5° to 12.6°.